A Comparative Analysis of Different Color Spaces for Paddy Maturity Assessment Using Drone Imagery
DOI:
https://doi.org/10.11113/mjfas.v21n3.3875Keywords:
Color space, drone imagery, image processing, machine learning, paddy maturity.Abstract
Paddy is a staple food for a large portion of the global population with Asia accounting for about 90% of the world's rice production. The accurate detection of paddy maturity is important to optimizing harvest time, ensuring maximum yield and reducing post-harvest losses. Traditional methods of assessing paddy ripeness are labour-intensive and prone to human error, hence required the development of efficient and automation approaches. This study explores the effectiveness of drone imagery and image processing to assess paddy maturity at two ripeness stages which are unripe and ripe (ready for immediate harvest). High-resolution images of paddy fields in a district of an ASEAN country were captured and processed using MATLAB software to analyze four color spaces include RGB, HSV, YCbCr and L*a*b*. The results show that the RGB and HSV color spaces reflect shifts in red/green intensities and hues during ripening. YCbCr shows the changes in chrominance components between the unripe and ripe stages. However, the L*a*b* color space proved to be the most effective, offering the highest distinction between ripe and unripe paddy in L*, a*, and b* values, which closely align with the expected visual ripeness characteristics. This study has suggested that integrating this method with machine learning could enable real-time, automated crop monitoring, improving harvest timing and overall crop management.
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Copyright (c) 2025 Ji Loun Tan, Mastang Tanra, Muhammad Mukhlisin, Amin Suharjono, Irfan Mujahidin, Catur Budi Waluyo, Rizkha Ajeng Rochmatika, Fatardho Zudhi, Norhana Arsad
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